Evidence map›Paper›PMID 41128874›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Deep learning models for lumbar spinal stenosis on MRI: model comparison and clinical benchmarking.

You Jun Lee, Changshuo Liu, Yong Han Ting, Andrew Makmur, Weizhong Jonathan Sng, Amanda J L Cheng, Jiong Hao Tan, Alex Quok An Teo, Zongchen Li, Alvin Hong Zhi Ng and 9 more

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Impact of frailty on surgical outcomes in degenerative cervical myelopathy: a systematic review and meta-analysis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

You Jun Lee *Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
Changshuo Liu *Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore.
Yong Han TingDepartment of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
Andrew MakmurDepartment of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
Weizhong Jonathan SngDepartment of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
Amanda J L ChengDepartment of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
Jiong Hao TanNational University Spine Institute, Department of Orthopaedic Surgery, National University Hospital, Singapore, Singapore.
Alex Quok An TeoNational University Spine Institute, Department of Orthopaedic Surgery, National University Hospital, Singapore, Singapore.
Zongchen LiDepartment of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
Alvin Hong Zhi NgDepartment of Radiology, Ng Teng Fong General Hospital, 1 Jurong East Street 21, Singapore, 609606, Singapore.
Aric LeeDepartment of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
Chongyan WangNational University Spine Institute, Department of Orthopaedic Surgery, National University Hospital, Singapore, Singapore.
Xinyi LimNational University Spine Institute, Department of Orthopaedic Surgery, National University Hospital, Singapore, Singapore.
Qai Ven YapBiostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore.
Joey Chan Yiing BehDepartment of Radiology, Ng Teng Fong General Hospital, 1 Jurong East Street 21, Singapore, 609606, Singapore.
Shuxun LinDivision of Spine Surgery, Department of Orthopaedic Surgery, Ng Teng Fong General Hospital, 1 Jurong East Street 21, Singapore, 609606, Singapore.
Naresh KumarNational University Spine Institute, Department of Orthopaedic Surgery, National University Hospital, Singapore, Singapore.
Beng Chin OoiDepartment of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore.
James Thomas Patrick Decourcy HallinanDepartment of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore. james_hallinan@nuhs.edu.sg.ORCID 0000-0002-0082-9033

Funding

National Medical Research Council CIAINV23jan-0001, MOH-001405
6 · The paper itself

Abstract

purposeTo compare deep learning models of different architecture for automated lumbar spinal stenosis classification on MRI and benchmark their performance against radiologists and orthopedists.

methodsLumbar spine MRI studies from Sep-2015 to Sep-2019 were retrospectively obtained. Exclusion criteria included previous spinal instrumentation, suboptimal image quality, post-gadolinium studies, and severe scoliosis. Axial T2-weighted and sagittal T1-weighted images were used. Studies were split into training/validation and test sets. An external test set of 100 studies was used. Training data were labelled by 4 radiologists using predefined gradings. Two models, CNN-based and transformer-based, were developed. Consensus labelling by two expert spine radiologists served as the reference standard. Test sets were labelled by 8 participants (2 general radiologists, 2 radiologists-in-training, 2 orthopedists, 2 orthopedists-in-training). Detection recall (%), interrater agreement (Gwet κ), sensitivity, and specificity were evaluated.

results564 MRI lumbar spines were included (mean age = 52 ± 19[SD]; 302 women), with 464(82%) and 100(18%) for training/validation and internal testing, respectively. Both models showed high recall for all regions of interest (> 94%), similar to participants. Dichotomous classification (normal/mild vs. moderate/severe) by the CNN model, transformer model, and participants showed respective kappas for central canal 0.99/0.99/0.97-0.98, lateral recesses 0.98/0.94/0.81-0.94, and neural foramina 0.98/0.95/0.91-0.95 on internal testing (p < 0.001); for central canal 0.99/0.97/0.92-0.97, lateral recess 0.97/0.90/0.61-0.91, and neural foramina 0.99/0.94/0.87-0.93 on external testing (p < 0.001).

conclusionThe CNN model showed superior performance, and the transformer model showed similar to superior performance compared to clinicians for classifying lumbar spinal stenosis. These models could assist clinicians in report generation, surgical planning and education.

Indexed as

Deep LearningLumbar VertebraeMagnetic Resonance ImagingSpinal StenosisAdultAgedBenchmarkingFemaleHumansMaleMiddle AgedRetrospective StudiesArtificial intelligence (AI)Automated classificationClinical benchmarkingConvolutional neural network (CNN)Deep learning (DL)Lumbar spinal stenosis (LSS)Machine learning (ML)Magnetic resonance imaging (MRI)Transformer model

Identifiers

PMID41128874

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.